How predictive analytics will make social networks money

You’ve all likely been experiencing a deluge of information coming at you online in recent years. An overwhelming number of status updates, emails, tagged images and so forth. You’ve all probably also seen, and potentially been alarmed by, the growing accuracy of targeted advertisements, “People You May Know,” and other “offers” online. As irrelevant information has exploded online, so too has the market for the delivery of targeted offers and information. Social networks, in theory and in practice, make people very exposed to contact and influence. Without precise models people will continue to be bombarded with offers and other information. Predictive analytics, a branch of data mining concerned with predicting future probabilities and trends, applies a filter to what gets sent and delivered to individuals delivering on a better promise of social networking to add real value to daily life while making money as well.

Some specific ways in which predictive analytics will make social networks money include:

-Recruiting: Of all of the recruiting sites out there on the web from LinkedIn to SelectMinds to Monster, each promises to be able to match candidates with job requirements in unique and increasingly accurate ways. Predictive analytics is at the core of their business model as it automates the process of making these matches. When a recruiter posts a job description, a predictive algorithm runs through candidates and calculates compatibility. The technology is, in many cases, embedded in search applications. The most accurate and efficient of these analytics will deliver the most value and see the greatest adoption over time. Those recruiting and talent acquisition sites that allow businesses to leverage the existing social networks of their current and former employees are the best positioned to monetize their users’ employment data in new ways. Businesses can get value from these existing networks without the time and resource commitment it takes to build their own.

-Sentiment Analysis: As sites like Twitter and Facebook gain value to the business world many companies have cropped up to analyze and establish what the sentiment is of the collective intelligence online as well as an individuals influence and authority. Companies including Klout, ViralHeat, and Radian6 all scan blogs and other social media channels with predictive models to determine if the content surrounding a brand or person is negative, positive or neutral. As this information and the statistics behind it, from companies like GraphEdge, are worth more to businesses of all sizes we expect to see these companies grow rapidly.

-Market Fluctuation: Social media channels are anything but non-exclusive – everyone from day traders to retail investors to analysts are cruising around on Twitter and Facebook. What these types of people say and do online is not insignificant in an era when Flash Crashes and Fat Fingers are being closely scrutinized and regulated. New predictive models are cropping up to predict stock fluctuations based on Twitter posts. Similar to sentiment analysis, these companies are able to harness this data and look at the total number of Tweets as well as positive and negative comments to predict whether a stock price will go up or down. These types of companies will become a hot commodity as even investors begin to rely upon the wisdom of crowds.

-Recommendation Engine: No one likes to be bombarded with irrelevant offers and content while using their favorite social network. But the more active you are online, the more effectively predictive analytics can work to deliver targeted and relevant offers. Sometimes it feels like Facebook knows you better than you know yourself. RSVP’ed yes for that big gala? You may get see a discount offer for Saks. Female between the ages of 18-34? Let a Facebook ad tell you how to lose those extra inches around your waist. These offers are no longer random and therefore they are increasingly effective. Leveraging the existing data from your previous activity to predict what will happen in the future is becoming, rightly, more prevalent and valuable to social networks who can sell this promise to businesses and intermediaries.

-Location-Based Marketing: Do you walk down the same street at dinner time everyday? Wish restaurants on that street would compete in real-time for your business? As social networks add in more location-aware features like Facebook Places and whole new businesses are built on the promise of geo-location including SCVNGR and ShopKick, predictive analytics deliver increasingly necessary visibility into where groups and individuals will be and when, not to mention what their interests may be. For businesses there is big money to be spent on location-based advertising big money to be spent on location-based advertising in the coming years. As a result, social networks can run their existing location data through predictive models to provide companies with future insights into where to allocate their marketing and advertising budgets for the biggest returns.

Dr. Rado Kotorov is Chief Innovation Officer at Information Builders, responsible for emerging reporting, analytic and visualization technologies. Prior to joining Information Builders, he managed the implementation of BI solutions and decision-support systems, data warehouses, and customer applications. He has developed analytic models and applications for the pharmaceutical, retail, CPG, financial, and automotive industries. Kotorov has a PhD in decision and game theory and economics from Bowling Green State University. He has published on business processes, emerging technologies, CRM, KM, innovation and entrepreneurship.

The opinions expressed herein or statements made in the above column are solely those of the author, and do not necessarily reflect the views of WTN Media, LLC. WTN accepts no legal liability or responsibility for any claims made or opinions expressed herein.